Abstract
Assessing the environmental hazard of potentially toxic elements in bottom sediments has always been based entirely on ground samples and laboratory tests. This approach is remarkably accurate, but it is slow, expensive, damaging, and spatially constrained, making it unsuitable for monitoring these parameters effectively. The main goal of the present study was to assess the quality of sediment samples collected from Lake Qaroun by using different groups of spectral reflectance indices (SRIs), integrating data-driven (Artificial Neural Networks; ANN) and multivariate analysis such as multiple linear regression (MLR) and partial least square regression (PLSR). Jetty cruises were carried out to collect sediment samples at 22 distinct sites over the entire Lake Qaroun, and subsequently 21 metals were analysed. Potential ecological risk index (RI), organic matter (OM), and pollution load index (PLI) of lake’s bottom sediments were subjected to evaluation. The results demonstrated that PLI showed that roughly 59% of lake sediments are polluted (PLI > 1), especially samples of eastern and southern sides of the lake’s central section, while 41% were unpolluted (PLI < 1), which composed samples of the western and western northern regions. The RI’s findings were that all the examined sediments pose a very high ecological risk (RI > 600). It is obvious that the three band spectral indices are more efficient in quantifying different investigated parameters. The results showed the efficiency of the three tested models to predict OM, PLI, and RI, revealing that the ANN is the best model to predict these parameters. For instance, the determination coefficient values of the ANN model of calibration datasets for predicting OM, PLI, and RI were 0.999, 0.999, and 0.999, while they were 0.960, 0.897, and 0.853, respectively, for the validation dataset. The validation dataset of the PLSR produced R2 values higher than with MLR for predicting PLI and RI. Finally, the study’s main conclusion is that combining ANN, PLSR, and MLR with proximal remote sensing could be a very effective tool for the detection of OM and pollution indices. Based on our findings, we suggest the created models are easy tools for forecasting these measured parameters.
Highlights
The potential toxic metals (PTMs) assessment in the bottom sediment of lakes, in the arid and semi-arid countries, has become a global topic with much attention from researchers [1,2]
PTMs distributions in sediments vary depending on particle size, oxide-hydroxide content, and organic matter (OM) content [15,17]
All PTMs in the lake sediment samples appeared to be accumulated at the exits of El-Bats and ElWadi drains, indicating high concentrations in the eastern and central parts of the lake
Summary
The potential toxic metals (PTMs) assessment in the bottom sediment of lakes, in the arid and semi-arid countries, has become a global topic with much attention from researchers [1,2]. The environmental pollution level has resulted from industry, agriculture, and unplanned urbanization activities determined through monitoring the degree of pollution in lakes [3,4]. On the other hand, provides useful information on a variety of pollution indicators [5]. Sediments scrupulously store evidence of human activity and are crucial in determining pollution sources, history, dispersion, and damage to the ecosystem [6–8]. The lake’s aquatic environmental conservation programs crucially need a clear explanation of the spatial distribution of PTMs in lake’s bottom sediments and inferring the possible ecological risk [9,10]. The investigation of PTMs in sediments close to contaminated areas may perhaps be used to assess their effects on ecosystems and determine the hazards posed by waste pumped into the environment [2,11,12]
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